A FRAMEWORK-BASED META-ANALYSIS OF ARTIFICIAL INTELLIGENCE-DRIVEN ERP SOLUTIONS FOR CIRCULAR AND SUSTAINABLE SUPPLY CHAINS

This study presents a framework-based meta-analysis of 124 peer-reviewed scholarly articles to explore how artificial intelligence (AI)-driven enterprise resource planning (ERP) systems are reshaping circular and sustainable supply chains (CSCs and SSCs). The research systematically followed the PRISMA protocol to ensure methodological rigor and transparency, with articles selected from leading academic databases such as Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The review investigates the integration of AI modules—such as machine learning, natural language processing, and robotics—within ERP platforms, with a focus on their application in enhancing predictive analytics, traceability, lifecycle management, and ESG (environmental, social, and governance) compliance. Sector-specific use cases from industries such as automotive, electronics, pharmaceuticals, agriculture, and fast-moving consumer goods (FMCG) were analyzed to assess implementation maturity and sustainability outcomes. Key findings reveal a concentration of research in high-tech sectors, limited longitudinal studies, underrepresentation of SMEs and developing economies, and fragmented use of conceptual frameworks. The study also identifies significant gaps in performance evaluation, interoperability, and cross-sectoral comparability. As a result, this meta-analysis proposes the need for an integrated evaluation framework that synthesizes technological, organizational, and sustainability dimensions of AI-ERP systems. This comprehensive synthesis not only advances academic understanding but also offers practical guidance for businesses, policymakers, and system architects aiming to foster digitally enabled, circular, and sustainable supply chains across global contexts.